Computerized system and method for automatically transforming and providing domain specific chatbot responses

ABSTRACT

Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The disclosure provides a computerized framework for automatically generating chatbot responses to produce domain-specific responses that mimic native styles unique to particular domains. The disclosed systems and methods construct domain-specific word-graphs based on account activity from specific domains and generate word-patterns. New words obtained from the patterns in the graph are introduced to transform the regular response. The graph is then pruned using data-driven thresholds in order to avoid spurious transformations, and paragraph vectors are also utilized to assign relevance scores to generated patterns such that only the patterns that are contextually similar to the original response (generic/regular response) are used. As result, the regular chatbot response is rewritten using an optimized set of patterns.

This application includes material that is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent disclosure, as it appears in thePatent and Trademark Office files or records, but otherwise reserves allcopyright rights whatsoever.

FIELD

The present disclosure relates generally to improving the performance ofcontent searching, generating, providing and/or hosting computerdevices, systems and/or platforms by modifying the capabilities andproviding non-native functionality to such devices, systems and/orplatforms for a novel and improved framework for automaticallygenerating and/or transforming chatbot responses to producedomain-specific responses that mimic native styles unique to particulardomains.

SUMMARY

With the tremendous growth in the field of Artificial Intelligence (AI),chatbots have become very popular tools on the internet for users tointeract with network platforms. Chatbots, as understood by those ofskill in the art, are computer programs that execute to conductconversations with users via auditory or textual methods. Such programsare designed to convincingly simulate how a human would behave as aconversational partner, thereby passing the Turing test. Chatbots (alsoreferred to as “chatterbots” interchangeably) are typically used indialog systems for various practical purposes including, for example,customer service or information acquisition. Chatbots can utilizesophisticated natural language processing techniques or mechanisms, andalso can scan for keywords within an input, then pull a reply with themost matching keywords, or the most similar wording pattern from adatabase. Conventional chatbots are part of virtual assistants such asGoogle® Assistant, and are accessed by many organizations applications,websites and on instant messaging platforms, such as FacebookMessenger®.

As such, there is a growing interest in building end-to-endconversational systems; however, recent development in AI and/or machinelearning technologies have fell short in enabling chatbots to generateresponses that mimic specific speaking styles of personalities. Forexample, some existing systems simply attempt to produce chatbotresponses in order for them to model human personas, which is restrictedto general human-like behavior and not specific persona styles.

The disclosed systems and methods provide a novel framework thatsimultaneously provides for chatbot responses to embody accurate answersto questions asked to the conversational agent while also transformingthese regular responses and/or generating new responses that mimicstyles specific to particular domains with which users can relate andare acquainted. For example, a user interested in fashion orentertainment would enjoy getting bot responses resembling the speakingstyles of fashionistas or entertainers, respectively.

Accordingly, in one or more embodiments, a method is disclosed for anovel, computerized framework for automatically generating and/ortransforming chatbot responses to produce domain-specific responses thatmimic native styles unique to particular domains (e.g., communities ofsimilar personalities such as, for example, politicians, singers, andthe like). The instant disclosure provides for computerized techniquesto construct domain-specific word-graphs using tweets posted fromTwitter® accounts (and/or any other type of network accessibleplatform/resource that enables learning/training of a system tounderstand language styles) that belong to users from specific domains,and use the graph to generate word-patterns. As discussed in more detailbelow, new words (obtained from the patterns in the graph) areintroduced to transform the regular response.

In some embodiments, the graph can be pruned (e.g., filtered, parsed,scraped and the like, as discussed in more detail below) usingdata-driven thresholds (e.g., such as co-occurrence, contextualsimilarity and linguistic quality metrics) in order to avoid spurioustransformations. In some embodiments, paragraph (or other types ofgrammatical identifiers) vectors are also utilized to assign relevancescores to generate word patterns, such that only the patterns that arecontextually similar to the original response (generic/regular response)are used. As result, only the best, most optimized set of patterns areused to rewrite the regular chatbot response.

In accordance with one or more embodiments, a non-transitorycomputer-readable storage medium is provided, the non-transitorycomputer-readable storage medium tangibly storing thereon, or havingtangibly encoded thereon, computer readable instructions that whenexecuted cause at least one processor to perform a method for a noveland improved framework for automatically generating and/or transformingchatbot responses to produce domain-specific responses that mimic nativestyles unique to particular domains.

In accordance with one or more embodiments, a system is provided thatcomprises one or more computing devices configured to providefunctionality in accordance with such embodiments. In accordance withone or more embodiments, functionality is embodied in steps of a methodperformed by at least one computing device. In accordance with one ormore embodiments, program code (or program logic) executed by aprocessor(s) of a computing device to implement functionality inaccordance with one or more such embodiments is embodied in, by and/oron a non-transitory computer-readable medium.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedisclosure will be apparent from the following description ofembodiments as illustrated in the accompanying drawings, in whichreference characters refer to the same parts throughout the variousviews. The drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating principles of the disclosure:

FIG. 1 is a schematic diagram illustrating an example of a networkwithin which the systems and methods disclosed herein could beimplemented according to some embodiments of the present disclosure;

FIG. 2 depicts is a schematic diagram illustrating an example of clientdevice in accordance with some embodiments of the present disclosure;

FIG. 3 is a schematic block diagram illustrating components of anexemplary system in accordance with embodiments of the presentdisclosure;

FIGS. 4A-4B are flowcharts illustrating steps performed in accordancewith some embodiments of the present disclosure;

FIG. 5 illustrates non-limiting example embodiments of the disclosedsystems and methods in accordance with some embodiments of the presentdisclosure;

FIG. 6 is a flowchart illustrating steps performed in accordance withsome embodiments of the present disclosure; and

FIG. 7 is a block diagram illustrating the architecture of an exemplaryhardware device in accordance with one or more embodiments of thepresent disclosure.

DESCRIPTION OF EMBODIMENTS

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, certain example embodiments. Subjectmatter may, however, be embodied in a variety of different forms and,therefore, covered or claimed subject matter is intended to be construedas not being limited to any example embodiments set forth herein;example embodiments are provided merely to be illustrative. Likewise, areasonably broad scope for claimed or covered subject matter isintended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The present disclosure is described below with reference to blockdiagrams and operational illustrations of methods and devices. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, can be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions can be provided to a processor of a general purposecomputer to alter its function as detailed herein, a special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implement thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

These computer program instructions can be provided to a processor of: ageneral purpose computer to alter its function to a special purpose; aspecial purpose computer; ASIC; or other programmable digital dataprocessing apparatus, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, implement the functions/acts specified in the block diagramsor operational block or blocks, thereby transforming their functionalityin accordance with embodiments herein.

For the purposes of this disclosure a computer readable medium (orcomputer-readable storage medium/media) stores computer data, which datacan include computer program code (or computer-executable instructions)that is executable by a computer, in machine readable form. By way ofexample, and not limitation, a computer readable medium may comprisecomputer readable storage media, for tangible or fixed storage of data,or communication media for transient interpretation of code-containingsignals. Computer readable storage media, as used herein, refers tophysical or tangible storage (as opposed to signals) and includeswithout limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, CD-ROM, DVD, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

For the purposes of this disclosure the term “server” should beunderstood to refer to a service point which provides processing,database, and communication facilities. By way of example, and notlimitation, the term “server” can refer to a single, physical processorwith associated communications and data storage and database facilities,or it can refer to a networked or clustered complex of processors andassociated network and storage devices, as well as operating softwareand one or more database systems and application software that supportthe services provided by the server. Servers may vary widely inconfiguration or capabilities, but generally a server may include one ormore central processing units and memory. A server may also include oneor more mass storage devices, one or more power supplies, one or morewired or wireless network interfaces, one or more input/outputinterfaces, or one or more operating systems, such as Windows Server,Mac OS X, Unix, Linux, FreeBSD, or the like.

For the purposes of this disclosure a “network” should be understood torefer to a network that may couple devices so that communications may beexchanged, such as between a server and a client device or other typesof devices, including between wireless devices coupled via a wirelessnetwork, for example. A network may also include mass storage, such asnetwork attached storage (NAS), a storage area network (SAN), or otherforms of computer or machine readable media, for example. A network mayinclude the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), wire-line type connections, wirelesstype connections, cellular or any combination thereof. Likewise,sub-networks, which may employ differing architectures or may becompliant or compatible with differing protocols, may interoperatewithin a larger network. Various types of devices may, for example, bemade available to provide an interoperable capability for differingarchitectures or protocols. As one illustrative example, a router mayprovide a link between otherwise separate and independent LANs.

A communication link or channel may include, for example, analogtelephone lines, such as a twisted wire pair, a coaxial cable, full orfractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as may be known to those skilled in the art.Furthermore, a computing device or other related electronic devices maybe remotely coupled to a network, such as via a wired or wireless lineor link, for example.

For purposes of this disclosure, a “wireless network” should beunderstood to couple client devices with a network. A wireless networkmay employ stand-alone ad-hoc networks, mesh networks, Wireless LAN(WLAN) networks, cellular networks, or the like. A wireless network mayfurther include a system of terminals, gateways, routers, or the likecoupled by wireless radio links, or the like, which may move freely,randomly or organize themselves arbitrarily, such that network topologymay change, at times even rapidly.

A wireless network may further employ a plurality of network accesstechnologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, WirelessRouter (WR) mesh, or 2nd, 3rd, or 4th generation (2G, 3G, 4G or 5G)cellular technology, or the like. Network access technologies may enablewide area coverage for devices, such as client devices with varyingdegrees of mobility, for example.

For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

A computing device may be capable of sending or receiving signals, suchas via a wired or wireless network, or may be capable of processing orstoring signals, such as in memory as physical memory states, and may,therefore, operate as a server. Thus, devices capable of operating as aserver may include, as examples, dedicated rack-mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like. Servers may vary widely in configuration or capabilities,but generally a server may include one or more central processing unitsand memory. A server may also include one or more mass storage devices,one or more power supplies, one or more wired or wireless networkinterfaces, one or more input/output interfaces, or one or moreoperating systems, such as Windows Server, Mac OS X, Unix, Linux,FreeBSD, or the like.

For purposes of this disclosure, a client (or consumer or user) devicemay include a computing device capable of sending or receiving signals,such as via a wired or a wireless network. A client device may, forexample, include a desktop computer or a portable device, such as acellular telephone, a smart phone, a display pager, a radio frequency(RF) device, an infrared (IR) device an Near Field Communication (NFC)device, a Personal Digital Assistant (PDA), a handheld computer, atablet computer, a phablet, a laptop computer, a set top box, a wearablecomputer, smart watch, an integrated or distributed device combiningvarious features, such as features of the forgoing devices, or the like.

A client device may vary in terms of capabilities or features. Claimedsubject matter is intended to cover a wide range of potentialvariations. For example, a simple smart phone, phablet or tablet mayinclude a numeric keypad or a display of limited functionality, such asa monochrome liquid crystal display (LCD) for displaying text. Incontrast, however, as another example, a web-enabled client device mayinclude a high-resolution screen, one or more physical or virtualkeyboards, mass storage, one or more accelerometers, one or moregyroscopes, global positioning system (GPS) or otherlocation-identifying type capability, or a display with a high degree offunctionality, such as a touch-sensitive color 2D or 3D display, forexample.

A client device may include or may execute a variety of operatingsystems, including a personal computer operating system, such as aWindows, iOS or Linux, or a mobile operating system, such as iOS,Android, or Windows Mobile, or the like.

A client device may include or may execute a variety of possibleapplications, such as a client software application enablingcommunication with other devices, such as communicating one or moremessages, such as via email, for example Yahoo! ® Mail, short messageservice (SMS), or multimedia message service (MMS), for exampleYahoo!Messenger®, including via a network, such as a social network,including, for example, Tumblr®, Facebook®, LinkedIn®, Twitter®,Flickr®, or Google+®, Instagram®, to provide only a few possibleexamples. A client device may also include or execute an application tocommunicate content, such as, for example, textual content, multimediacontent, or the like. A client device may also include or execute anapplication to perform a variety of possible tasks, such as browsing,searching, playing, streaming or displaying various forms of content,including locally stored or uploaded images and/or video, or games (suchas fantasy sports leagues). The foregoing is provided to illustrate thatclaimed subject matter is intended to include a wide range of possiblefeatures or capabilities.

As discussed herein, reference to an “advertisement” should beunderstood to include, but not be limited to, digital media contentembodied as a media item that provides information provided by anotheruser, service, third party, entity, and the like. Such digital adcontent can include any type of known or to be known media renderable bya computing device, including, but not limited to, video, text, audio,images, and/or any other type of known or to be known multi-media itemor object. In some embodiments, the digital ad content can be formattedas hyperlinked multi-media content that provides deep-linking featuresand/or capabilities. Therefore, while some content is referred to as anadvertisement, it is still a digital media item that is renderable by acomputing device, and such digital media item comprises content relayingpromotional content provided by a network associated party.

The principles described herein may be embodied in many different forms.The present disclosure provides a novel, computerized framework thatautomatically transforms chatbot responses into domain-specificresponses that mimic native styles unique to particular domains. Thedisclosed systems and methods addresses the problem of transformingfactual chatbot responses (referred to as regular response) to amodified response that is compatible with a domain-specificcommunication style—for example, speaking or language styles (orpatterns) of politicians. The goal of the disclosed systems and methodsis to preserve the content of the original response, but alter its styleby replacing existing word sequences in regular chatbot responses withdetermined stylized words or word sequences, thereby mimickingdomain-specific styles.

By way of background, conventional conversational agents have recentlyreceived major attention from researchers, especially from theperspective of Natural Language Generation (NLG). Receiving accurateresponses from a chatbot is essential; however, bots that havefunctionality for mimicking personas or specific speaking styles areabsent in the field. As evidenced from the discussion herein, havingsuch functionality can enable a website or network location (e.g.,application, service or platform) to increase its capability ofretaining its users, in that providing users with accurate, stylizedresponses can address the needs of a user's requests and entertainmentvalue.

For example, one conventional attempt to mimic human-like conversationsis to use a persona-model using a deep neural-network model. However,such system does not differentiate between speakers from differentdomains and does not generate text conforming to specific speakingstyles. In contrast to these systems, the instant disclosure does notgenerate responses in conversations, but modifies regular responses tofit domain-specific styles, as discussed in more detail below.

In another example, some systems' chat and virtual agents select thebest possible response from a list of pre-populated responses andtemplates. And, some systems model responses based on “five-factorpersonalities” in order to model characters; however, such systems arefocused solely on a response perceiving personality traits of acharacter rather than persona-based content generation.

In contrast to the above-mentioned approaches, the disclosed systems andmethods provide a novel computerized response generation environmentthat is completely data-driven with the purpose of transforming regularresponses (e.g., original factual responses as in conventional systems)by modifying them to include stylized content specific to a particulardomain without relying on predefined responses or templates. Therefore,according to embodiments of the instant disclosure, the disclosedsystems and methods provide a novel end-to-end conversational enginethat provides chatbot responses that accurately and factually answersquestions with responses that mimic styles specific to particulardomains.

For purposes of this disclosure, the discussed embodiments willcenter-around two domains and styles—politics and entertainment;however, it should not be construed as limiting, as any type ofdomain/style can be utilized and/or leveraged in producing the generatedstylized responses discussed herein. For example, such domains/stylescan also include, but are not limited to, fashion, business, regional(country, northeastern, Boston accents/cadences, and the like), slang,elementary, adult, radio hosts, singers, actors, sports figures,commentators, and the like, or some combination thereof.

According to embodiments of the instant disclosure, as discussed in moredetail below, a user enters a query with a chatbot asking for aparticular piece of content/information. For example, “What is theweather today?” In response to this query, the disclosed systems andmethods will retrieve the accurate/factual response (e.g., “The weatherin New York City is 72 degrees and Sunny.”) However, instead of simplyoutputting this response, as in conventional systems, the disclosedsystems and methods automatically transform the initial chatbot responseto produce domain-specific response that mimics a native style unique tothe particular domain from which the query was entered. Therefore, sincethe user entered the query from a domain, for example, associated with anews source (e.g., cnn.com, which provides political analysis), theresponse can be stylized according to a political undertone such that itincludes the type of rhetoric a politician would use when answering aquestion. For example, as a politician would typically say, the chatbotresponse can be modified to state “Good afternoon Sir, the weather todayin the Big Apple appears to be 72 degrees and Sunny, hope you enjoy yourday.” Thus, the transformed response retains the factual content butadds a distinctive style easily identifiable and attributable to aspecific domain.

As discussed in more detail below at least in relation to FIG. 6,according to some embodiments, information associated with, derivedfrom, or otherwise identified from, during or as a result of generatedchatbot response, as discussed herein, can be used for monetizationpurposes and targeted advertising when providing, delivering or enablingdevices access or output a response. Providing targeted advertising tousers associated with such discovered content can lead to an increasedclick-through rate (CTR) of such ads and/or an increase in theadvertiser's return on investment (ROI) for serving such contentprovided by third parties (e.g., digital advertisement content providedby an advertiser, where the advertiser can be a third party advertiser,or an entity directly associated with or hosting the systems and methodsdiscussed herein).

Certain embodiments will now be described in greater detail withreference to the figures. In general, with reference to FIG. 1, a system100 in accordance with an embodiment of the present disclosure is shown.FIG. 1 shows components of a general environment in which the systemsand methods discussed herein may be practiced. Not all the componentsmay be required to practice the disclosure, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the disclosure. As shown, system 100 of FIG.1 includes local area networks (“LANs”)/wide area networks(“WANs”)—network 105, wireless network 110, mobile devices (clientdevices) 102-104 and client device 101. FIG. 1 additionally includes avariety of servers, such as content server 106, application (or “App”)server 108, search server 120 and advertising (“ad”) server 130.

One embodiment of mobile devices 102-104 is described in more detailbelow. Generally, however, mobile devices 102-104 may include virtuallyany portable computing device capable of receiving and sending a messageover a network, such as network 105, wireless network 110, or the like.Mobile devices 102-104 may also be described generally as client devicesthat are configured to be portable. Thus, mobile devices 102-104 mayinclude virtually any portable computing device capable of connecting toanother computing device and receiving information. Such devices includemulti-touch and portable devices such as, cellular telephones, smartphones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers, laptopcomputers, wearable computers, smart watch, tablet computers, phablets,integrated devices combining one or more of the preceding devices, andthe like. As such, mobile devices 102-104 typically range widely interms of capabilities and features. For example, a cell phone may have anumeric keypad and a few lines of monochrome LCD display on which onlytext may be displayed. In another example, a web-enabled mobile devicemay have a touch sensitive screen, a stylus, and an HD display in whichboth text and graphics may be displayed.

A web-enabled mobile device may include a browser application that isconfigured to receive and to send web pages, web-based messages, and thelike. The browser application may be configured to receive and displaygraphics, text, multimedia, and the like, employing virtually any webbased language, including a wireless application protocol messages(WAP), and the like. In one embodiment, the browser application isenabled to employ Handheld Device Markup Language (HDML), WirelessMarkup Language (WML), WMLScript, JavaScript, Standard GeneralizedMarkup Language (SMGL), HyperText Markup Language (HTML), DynamicHyperText Markup Language (DHTML), eXtensible Markup Language (XML), andthe like, to display and send a message.

Mobile devices 102-104 also may include at least one client applicationthat is configured to receive content from another computing device. Theclient application may include a capability to provide and receivetextual content, graphical content, audio content, and the like. Theclient application may further provide information that identifiesitself, including a type, capability, name, and the like. In oneembodiment, mobile devices 102-104 may uniquely identify themselvesthrough any of a variety of mechanisms, including a phone number, MobileIdentification Number (MIN), an electronic serial number (ESN), or othermobile device identifier.

In some embodiments, mobile devices 102-104 may also communicate withnon-mobile client devices, such as client device 101, or the like. Inone embodiment, such communications may include sending and/or receivingmessages, searching for, viewing and/or sharing photographs, audioclips, video clips, or any of a variety of other forms ofcommunications. Client device 101 may include virtually any computingdevice capable of communicating over a network to send and receiveinformation. The set of such devices may include devices that typicallyconnect using a wired or wireless communications medium such as personalcomputers, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, or the like. Thus, client device 101may also have differing capabilities for displaying navigable views ofinformation.

Client devices 101-104 computing device may be capable of sending orreceiving signals, such as via a wired or wireless network, or may becapable of processing or storing signals, such as in memory as physicalmemory states, and may, therefore, operate as a server. Thus, devicescapable of operating as a server may include, as examples, dedicatedrack-mounted servers, desktop computers, laptop computers, set topboxes, integrated devices combining various features, such as two ormore features of the foregoing devices, or the like.

Wireless network 110 is configured to couple mobile devices 102-104 andits components with network 105. Wireless network 110 may include any ofa variety of wireless sub-networks that may further overlay stand-alonead-hoc networks, and the like, to provide an infrastructure-orientedconnection for mobile devices 102-104. Such sub-networks may includemesh networks, Wireless LAN (WLAN) networks, cellular networks, and thelike.

Network 105 is configured to couple content server 106, applicationserver 108, or the like, with other computing devices, including, clientdevice 101, and through wireless network 110 to mobile devices 102-104.Network 105 is enabled to employ any form of computer readable media forcommunicating information from one electronic device to another. Also,network 105 can include the Internet in addition to local area networks(LANs), wide area networks (WANs), direct connections, such as through auniversal serial bus (USB) port, other forms of computer-readable media,or any combination thereof. On an interconnected set of LANs, includingthose based on differing architectures and protocols, a router acts as alink between LANs, enabling messages to be sent from one to another,and/or other computing devices.

Within the communications networks utilized or understood to beapplicable to the present disclosure, such networks will employ variousprotocols that are used for communication over the network. Signalpackets communicated via a network, such as a network of participatingdigital communication networks, may be compatible with or compliant withone or more protocols. Signaling formats or protocols employed mayinclude, for example, TCP/IP, UDP, QUIC (Quick UDP Internet Connection),DECnet, NetBEUI, IPX, APPLETALK™, or the like. Versions of the InternetProtocol (IP) may include IPv4 or IPv6. The Internet refers to adecentralized global network of networks. The Internet includes localarea networks (LANs), wide area networks (WANs), wireless networks, orlong haul public networks that, for example, allow signal packets to becommunicated between LANs. Signal packets may be communicated betweennodes of a network, such as, for example, to one or more sites employinga local network address. A signal packet may, for example, becommunicated over the Internet from a user site via an access nodecoupled to the Internet. Likewise, a signal packet may be forwarded vianetwork nodes to a target site coupled to the network via a networkaccess node, for example. A signal packet communicated via the Internetmay, for example, be routed via a path of gateways, servers, etc. thatmay route the signal packet in accordance with a target address andavailability of a network path to the target address.

According to some embodiments, the present disclosure may also beutilized within or accessible to an electronic social networking site. Asocial network refers generally to an electronic network of individuals,such as, but not limited to, acquaintances, friends, family, colleagues,or co-workers, that are coupled via a communications network or via avariety of sub-networks. Potentially, additional relationships maysubsequently be formed as a result of social interaction via thecommunications network or sub-networks. In some embodiments, multi-modalcommunications may occur between members of the social network.Individuals within one or more social networks may interact orcommunication with other members of a social network via a variety ofdevices. Multi-modal communication technologies refers to a set oftechnologies that permit interoperable communication across multipledevices or platforms, such as cell phones, smart phones, tabletcomputing devices, phablets, personal computers, televisions, set-topboxes, SMS/MMS, email, instant messenger clients, forums, socialnetworking sites, or the like.

In some embodiments, the disclosed networks 110 and/or 105 may comprisea content distribution network(s). A “content delivery network” or“content distribution network” (CDN) generally refers to a distributedcontent delivery system that comprises a collection of computers orcomputing devices linked by a network or networks. A CDN may employsoftware, systems, protocols or techniques to facilitate variousservices, such as storage, caching, communication of content, orstreaming media or applications. A CDN may also enable an entity tooperate or manage another's site infrastructure, in whole or in part.

The content server 106 may include a device that includes aconfiguration to provide content via a network to another device. Acontent server 106 may, for example, host a site, service or anassociated application, such as, an email or messaging platform (e.g.,Yahoo!® Mail), a social networking site, a photo sharing site/service(e.g., Tumblr®), a search platform or site, or a personal user site(such as a blog, vlog, online dating site, and the like) and the like. Acontent server 106 may also host a variety of other sites, including,but not limited to business sites, educational sites, dictionary sites,encyclopedia sites, wikis, financial sites, government sites, and thelike. Devices that may operate as content server 106 include personalcomputers desktop computers, multiprocessor systems,microprocessor-based or programmable consumer electronics, network PCs,servers, and the like. Likewise, the search server 120 may include adevice that includes a configuration to provide content via a network toanother device.

In some embodiments, the content server 106 (and/or other servers 108,120 and 130, for example), can host the chatbot engine 300 discussedbelow that enables the content provided by such server(s) to betransformed/generated according the disclosed systems and methodsdiscussed herein.

Content server 106 can further provide a variety of services thatinclude, but are not limited to, streaming and/or downloading mediaservices, search services, email services, photo services, web services,social networking services, news services, third-party services, audioservices, video services, instant messaging (IM) services, SMS services,MMS services, FTP services, voice over IP (VOIP) services, or the like.Such services, for example a mail application and/or email-platform, canbe provided via the application server 108, whereby a user is able toutilize such service upon the user being authenticated, verified oridentified by the service. Examples of content may include videos, text,audio, images, or the like, which may be processed in the form ofphysical signals, such as electrical signals, for example, or may bestored in memory, as physical states, for example.

In a similar manner as the content server 106, the search server 120 mayinclude a device that includes a configuration to provide content via anetwork to another device. The search server 120 can, for example, hosta site, service or an associated application, such as, an search engine(e.g., Yahoo! ® Search, Bing®, Google Search®, and the like), a socialnetworking site, a photo sharing site/service (e.g., Tumblr®), and thelike. Additionally, the search server 120 can further provide a varietyof services similar to those outlined above for the content server 106.

An ad server 130 comprises a server that stores online advertisementsfor presentation to users. “Ad serving” refers to methods used to placeonline advertisements on websites, in applications, or other placeswhere users are more likely to see them, such as during an onlinesession or during computing platform use, for example. Variousmonetization techniques or models may be used in connection withsponsored advertising, including advertising associated with user. Suchsponsored advertising includes monetization techniques includingsponsored search advertising, non-sponsored search advertising,guaranteed and non-guaranteed delivery advertising, adnetworks/exchanges, ad targeting, ad serving and ad analytics. Suchsystems can incorporate near instantaneous auctions of ad placementopportunities during web page creation, (in some cases in less than 500milliseconds) with higher quality ad placement opportunities resultingin higher revenues per ad. That is advertisers will pay higheradvertising rates when they believe their ads are being placed in oralong with highly relevant content that is being presented to users.Reductions in the time needed to quantify a high quality ad placementoffers ad platforms competitive advantages. Thus higher speeds and morerelevant context detection improve these technological fields.

For example, a process of buying or selling online advertisements mayinvolve a number of different entities, including advertisers,publishers, agencies, networks, or developers. To simplify this process,organization systems called “ad exchanges” may associate advertisers orpublishers, such as via a platform to facilitate buying or selling ofonline advertisement inventory from multiple ad networks. “Ad networks”refers to aggregation of ad space supply from publishers, such as forprovision en masse to advertisers. For web portals like Yahoo!®,advertisements may be displayed on web pages or in apps resulting from auser-defined search based at least in part upon one or more searchterms. Advertising may be beneficial to users, advertisers or webportals if displayed advertisements are relevant to interests of one ormore users. Thus, a variety of techniques have been developed to inferuser interest, user intent or to subsequently target relevantadvertising to users. One approach to presenting targeted advertisementsincludes employing demographic characteristics (e.g., age, income,gender, occupation, etc.) for predicting user behavior, such as bygroup. Advertisements may be presented to users in a targeted audiencebased at least in part upon predicted user behavior(s).

Another approach includes profile-type ad targeting. In this approach,user profiles specific to a user may be generated to model userbehavior, for example, by tracking a user's path through a web site ornetwork of sites, and compiling a profile based at least in part onpages or advertisements ultimately delivered. A correlation may beidentified, such as for user purchases, for example. An identifiedcorrelation may be used to target potential purchasers by targetingcontent or advertisements to particular users. During presentation ofadvertisements, a presentation system may collect descriptive contentabout types of advertisements presented to users. A broad range ofdescriptive content may be gathered, including content specific to anadvertising presentation system. Advertising analytics gathered may betransmitted to locations remote to an advertising presentation systemfor storage or for further evaluation. Where advertising analyticstransmittal is not immediately available, gathered advertising analyticsmay be stored by an advertising presentation system until transmittal ofthose advertising analytics becomes available.

Servers 106, 108, 120 and 130 may be capable of sending or receivingsignals, such as via a wired or wireless network, or may be capable ofprocessing or storing signals, such as in memory as physical memorystates. Devices capable of operating as a server may include, asexamples, dedicated rack-mounted servers, desktop computers, laptopcomputers, set top boxes, integrated devices combining various features,such as two or more features of the foregoing devices, or the like.Servers may vary widely in configuration or capabilities, but generally,a server may include one or more central processing units and memory. Aserver may also include one or more mass storage devices, one or morepower supplies, one or more wired or wireless network interfaces, one ormore input/output interfaces, or one or more operating systems, such asWindows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

In some embodiments, users are able to access services provided byservers 106, 108, 120 and/or 130. This may include in a non-limitingexample, authentication servers, search servers, email servers, socialnetworking services servers, SMS servers, IM servers, MMS servers,exchange servers, photo-sharing services servers, and travel servicesservers, via the network 105 using their various devices 101-104. Insome embodiments, applications, such as a search application (e.g.,Yahoo!® Search), mail or messaging application (e.g., Yahoo!® Mail,Yahoo! ® Messenger), a photo sharing/user-generated content (UGC)application (e.g., Flickr®, Tumblr®, Instagram® and the like), astreaming video application (e.g., Netflix®, Hulu®, iTunes®, AmazonPrime®, HBO Go®, and the like), blog, photo or social networkingapplication (e.g., Facebook®, Twitter® and the like), and the like, canbe hosted by the application server 108 (or content server 106, searchserver 120 and the like).

Thus, the application server 108 can store various types of applicationsand application related information including application data and userprofile information (e.g., identifying and behavioral informationassociated with a user). It should also be understood that contentserver 106 can also store various types of data related to the contentand services provided by content server 106 in an associated database107, as discussed in more detail below. Embodiments exist where thenetwork 105 is also coupled with/connected to a Trusted Search Server(TSS) which can be utilized to render content in accordance with theembodiments discussed herein. Embodiments exist where the TSSfunctionality can be embodied within servers 106, 108, 120 and/or 130.

Moreover, although FIG. 1 illustrates servers 106, 108, 120 and 130 assingle computing devices, respectively, the disclosure is not solimited. For example, one or more functions of servers 106, 108, 120and/or 130 may be distributed across one or more distinct computingdevices. Moreover, in one embodiment, servers 106, 108, 120 and/or 130may be integrated into a single computing device, without departing fromthe scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating a client device showing anexample embodiment of a client device that may be used within thepresent disclosure. Client device 200 may include many more or lesscomponents than those shown in FIG. 2. However, the components shown aresufficient to disclose an illustrative embodiment for implementing thepresent disclosure. Client device 200 may represent, for example, clientdevices discussed above in relation to FIG. 1.

As shown in the figure, Client device 200 includes a processing unit(CPU) 222 in communication with a mass memory 230 via a bus 224. Clientdevice 200 also includes a power supply 226, one or more networkinterfaces 250, an audio interface 252, a display 254, a keypad 256, anilluminator 258, an input/output interface 260, a haptic interface 262,an optional global positioning systems (GPS) receiver 264 and acamera(s) or other optical, thermal or electromagnetic sensors 266.Device 200 can include one camera/sensor 266, or a plurality ofcameras/sensors 266, as understood by those of skill in the art. Thepositioning of the camera(s)/sensor(s) 266 on device 200 can change perdevice 200 model, per device 200 capabilities, and the like, or somecombination thereof.

Power supply 226 provides power to Client device 200. A rechargeable ornon-rechargeable battery may be used to provide power. The power mayalso be provided by an external power source, such as an AC adapter or apowered docking cradle that supplements and/or recharges a battery.

Client device 200 may optionally communicate with a base station (notshown), or directly with another computing device. Network interface 250includes circuitry for coupling Client device 200 to one or morenetworks, and is constructed for use with one or more communicationprotocols and technologies as discussed above. Network interface 250 issometimes known as a transceiver, transceiving device, or networkinterface card (NIC).

Audio interface 252 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 252 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others and/or generate an audio acknowledgementfor some action. Display 254 may be a liquid crystal display (LCD), gasplasma, light emitting diode (LED), or any other type of display usedwith a computing device. Display 254 may also include a touch sensitivescreen arranged to receive input from an object such as a stylus or adigit from a human hand.

Keypad 256 may comprise any input device arranged to receive input froma user. For example, keypad 256 may include a push button numeric dial,or a keyboard. Keypad 256 may also include command buttons that areassociated with selecting and sending images. Illuminator 258 mayprovide a status indication and/or provide light. Illuminator 258 mayremain active for specific periods of time or in response to events. Forexample, when illuminator 258 is active, it may backlight the buttons onkeypad 256 and stay on while the client device is powered. Also,illuminator 258 may backlight these buttons in various patterns whenparticular actions are performed, such as dialing another client device.Illuminator 258 may also cause light sources positioned within atransparent or translucent case of the client device to illuminate inresponse to actions.

Client device 200 also comprises input/output interface 260 forcommunicating with external devices, such as a headset, or other inputor output devices not shown in FIG. 2. Input/output interface 260 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like. Haptic interface 262 is arranged to providetactile feedback to a user of the client device. For example, the hapticinterface may be employed to vibrate client device 200 in a particularway when the Client device 200 receives a communication from anotheruser.

Optional GPS transceiver 264 can determine the physical coordinates ofClient device 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 264 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), E-OTD, CI, SAI, ETA, BSS or thelike, to further determine the physical location of Client device 200 onthe surface of the Earth. It is understood that under differentconditions, GPS transceiver 264 can determine a physical location withinmillimeters for Client device 200; and in other cases, the determinedphysical location may be less precise, such as within a meter orsignificantly greater distances. In one embodiment, however, Clientdevice may through other components, provide other information that maybe employed to determine a physical location of the device, includingfor example, a MAC address, Internet Protocol (IP) address, or the like.

Mass memory 230 includes a RAM 232, a ROM 234, and other storage means.Mass memory 230 illustrates another example of computer storage mediafor storage of information such as computer readable instructions, datastructures, program modules or other data. Mass memory 230 stores abasic input/output system (“BIOS”) 240 for controlling low-leveloperation of Client device 200. The mass memory also stores an operatingsystem 241 for controlling the operation of Client device 200. It willbe appreciated that this component may include a general purposeoperating system such as a version of UNIX, or LINUX™, or a specializedclient communication operating system such as Windows Client™, or theSymbian® operating system. The operating system may include, orinterface with a Java virtual machine module that enables control ofhardware components and/or operating system operations via Javaapplication programs.

Memory 230 further includes one or more data stores, which can beutilized by Client device 200 to store, among other things, applications242 and/or other data. For example, data stores may be employed to storeinformation that describes various capabilities of Client device 200.The information may then be provided to another device based on any of avariety of events, including being sent as part of a header during acommunication, sent upon request, or the like. At least a portion of thecapability information may also be stored on a disk drive or otherstorage medium (not shown) within Client device 200.

Applications 242 may include computer executable instructions which,when executed by Client device 200, transmit, receive, and/or otherwiseprocess audio, video, images, and enable telecommunication with a serverand/or another user of another client device. Other examples ofapplication programs or “apps” in some embodiments include browsers,calendars, contact managers, task managers, transcoders, photomanagement, database programs, word processing programs, securityapplications, spreadsheet programs, games, search programs, and soforth. Applications 242 may further include search client 245 that isconfigured to send, to receive, and/or to otherwise process a searchquery and/or search result using any known or to be known communicationprotocols. Although a single search client 245 is illustrated it shouldbe clear that multiple search clients may be employed. For example, onesearch client may be configured to enter a search query message, whereanother search client manages search results, and yet another searchclient is configured to manage serving digital content (e.g.,advertisements) or other forms of digital data associated with, but notlimited to, IMs, emails, and other types of known messages, or the like.

Having described the components of the general architecture employedwithin the disclosed systems and methods, the components' generaloperation with respect to the disclosed systems and methods will now bedescribed below with reference to FIGS. 3-5.

FIG. 3 is a block diagram illustrating the components for performing thesystems and methods discussed herein. FIG. 3 includes a chatbot engine300, network 315 and database 320. The chatbot engine 300 can be aspecial purpose machine or processor and could be hosted by a searchserver, application server, content server, social networking server,web server, messaging server, content provider, email service provider,ad server, user's computing device, and the like, or any combinationthereof.

According to some embodiments, chatbot engine 300 can be embodied as astand-alone application that executes on a user device. In someembodiments, the chatbot engine 300 can function as an applicationinstalled on the user's device, and in some embodiments, suchapplication can be a web-based application accessed by the user deviceover a network. In some embodiments, the chatbot engine 300 can beinstalled as an augmenting script, program or application to anothersearching, messaging and/or media content hosting/serving application,service or platform, such as, for example, Yahoo! Search, Yahoo! Mail,Yahoo!Messenger, Flickr®, Tumblr®, Twitter®, Instagram®, SnapChat®,Facebook®, and the like.

The database 320 can be any type of database or memory, and can beassociated with a content server on a network (e.g., content server 106,search server 120, ad server 130 or application server 108 from FIG. 1)or a user's device (e.g., device 101-104 or device 200 from FIGS. 1-2).Database 320 comprises a dataset of data and metadata associated withlocal and/or network information related to users, services,applications, content (e.g., images) and the like. Such information canbe stored and indexed in the database 320 independently and/or as alinked or associated dataset. As discussed above, it should beunderstood that the data (and metadata) in the database 320 can be anytype of information and type, whether known or to be known, withoutdeparting from the scope of the present disclosure.

According to some embodiments, database 320 can store data for users,i.e., user data. According to some embodiments, the stored user data caninclude, but is not limited to, information associated with a user'sprofile, user interests, user behavioral information, user attributes,user preferences or settings, user demographic information, userlocation information (i.e., past and present location(s) of the user,and future locations of the user (derived from a calendar or schedule ofthe user—e.g., planned activities), user biographic information, and thelike, or some combination thereof. In some embodiments, the user datacan also include, for purposes rendering and/or displaying images, userdevice information, including, but not limited to, device identifyinginformation, device capability information, voice/data carrierinformation, Internet Protocol (IP) address, applications installed orcapable of being installed or executed on such device, and/or any, orsome combination thereof. It should be understood that the data (andmetadata) in the database 320 can be any type of information related toa user, content, a device, an application, a service provider, a contentprovider, whether known or to be known, without departing from the scopeof the present disclosure.

According to some embodiments, database 320 can comprise informationassociated with content providers, such as, but not limited to, contentgenerating and hosting sites or providers that enable users to searchfor content, message (e.g., send or receive messages), upload, download,share, edit, comment or otherwise avail users to media content (e.g.,Yahoo!Search, Yahoo!@ Mail, Flickr®, Tumblr®, Twitter®, Instagram®,SnapChat®, Facebook®, and the like). Such sites may also enable users tosearch for and purchase products or services based on informationprovided by those sites, such as, for example, Amazon®, EBay® and thelike. In some embodiments, database 320 can comprise data and metadataassociated with such content information from one and/or an assortmentof media hosting sites.

In some embodiments, database 320 can comprise a lexicon of one or morewords, by way of non-limiting example, a vocabulary, dictionary orcatalogue of words/phrases (e.g., known or learned word combinations).As discussed below, the lexicon can be compiled based on socialnetworking, search and mail activity of users on a network. In someembodiments, the lexicon information housed within the database 320 canbe arranged in accordance with various known or to be known models inorder to preserve an efficient and accurate retrieval of terms withinthe database.

According to some embodiments, the lexicon in database 320 comprisesterms (i.e., words, phrases or paragraphs) arranged according to howthey were generated. For example, terms associated with search queriescan be organized according to when they were entered by a user and/orwhich other search terms were associated therewith. Terms associatedwith mail messages, for example, can be organized in accordance withother terms in the same or similar messages (e.g., group text within asingle message, group text associated with a message thread). Termsassociated with social networking activity can be organized inaccordance with their order, their topic and/or by which users and/orthe domain they are associated with, for example. In some embodiments,the words are arranged according to known or to be known languagemodels. The words in the lexicon can be assigned a unique identifier,such as, but not limited to, a number or value. It should be understoodthat generally no two words (or phrases) in a lexicon are associatedwith the same unique identifier. Thus, a unique identifier should beunique to one word/phrase in the lexicon.

According to some embodiments, n-grams may be encoded using such wordidentifiers. As understood by those of skill in the art, an n-graminvolves computational linguistics for a contiguous sequence of n-itemsfrom a given sequence of text. Thus, in the lexicon within database 320,the terms that are related to one another (e.g., terms in a search queryor terms in a message) can be arranged according to a language modelutilized for identifying the next item in such a sequence. It should beunderstood that any known or to be known arrangement or model (e.g.,Markov model) and/or algorithm can be used for arranging one or morewords, and identifying such one or more words in the database 320.

In some embodiments, database 320 can be specific to a user, a networkservice or platform, or a global lexicon (such as a generic orlearned/trained lexicon). Thus, in some embodiments, the lexicon ofterms in database 320 may be ranked or ordered according to the numberof times a user or users has used a term during a predetermined period.In some embodiments, the lexicon in database 320 can be based on auser's or users' behavior (e.g., past activity—for example, words orphrases used in social network messages at or above a threshold(frequency over a predetermined time) which takes precedence over globallexical norms and conventions). Therefore, in some embodiments, adetermined frequency for which a user uses a word or phrase online maybe utilized to organize how a lexicon stores or organizes words/phrases.

In some embodiments, the information stored in database 320 can berepresented as an n-dimensional vector (or feature vector) for eachstored term, where the information associated with the words (or text orkeywords) within each search and/or message corresponds to a node(s) onthe vector. Additionally, the information in database 320 can comprise,but is not limited to, social metrics associated with the information(e.g., popularity of the content or product—a number of views, shares,favorites, reviews or purchases), a title or comment(s) associated withthe information, tags, descriptions, quality of the content, recency ofthe content's upload and/or share(s), and the like. Such factors can bederived from information provided by the user, a service provider (e.g.,Yahoo!®), by the content/service providers providing content information(e.g., Tumblr®, Flickr®, or third party vendor sites), or by other thirdparty services (e.g., Twitter®, Facebook®, Instagram®, and the like, orthird party sites that enable users to purchase products from othervendors, such as Amazon®), or some combination thereof. In someembodiments, such additional factors can also be translated as nodes onthe n-dimensional vector for a respective search query, search resultand/or message.

As such, database 320 can store and index content information indatabase 320 as linked set of data and metadata, where the data andmetadata relationship can be stored as the n-dimensional vectordiscussed above. Such storage can be realized through any known or to beknown vector or array storage, including but not limited to, a hashtree, queue, stack, VList, or any other type of known or to be knowndynamic memory allocation technique or technology. While the discussionof some embodiments involves vector analysis of content information, asdiscussed above, the information can be analyzed, stored and indexedaccording to any known or to be known computational analysis techniqueor algorithm, such as, but not limited to, Word2Vec analysis, clusteranalysis, data mining, Bayesian network analysis, Hidden Markov models,artificial neural network analysis, logical model and/or tree analysis,and the like.

In some embodiments, database 320 can be a single database housinginformation associated with one or more devices, users, services and/orcontent providers, and in some embodiments, database 320 can beconfigured as a linked set of data stores that provides suchinformation, as each datastore in the set is associated with and/orunique to a specific user, device, service and/or content provider.

As discussed above, with reference to FIG. 1, the network 315 can be anytype of network such as, but not limited to, a wireless network, a localarea network (LAN), wide area network (WAN), the Internet, or acombination thereof. The network 315 facilitates connectivity of thechatbot engine 300, and the database of stored resources 320. Indeed, asillustrated in FIG. 3, the chatbot engine 300 and database 320 can bedirectly connected by any known or to be known method of connectingand/or enabling communication between such devices and resources.

The principal processor, server, or combination of devices thatcomprises hardware programmed in accordance with the special purposefunctions herein is referred to for convenience as chatbot engine 300,and includes a query module 302, word-graph construction module 304,transformation module 306 and output module 308. It should be understoodthat the engine(s) and modules discussed herein are non-exhaustive, asadditional or fewer engines and/or modules (or sub-modules) may beapplicable to the embodiments of the systems and methods discussed. Theoperations, configurations and functionalities of each module, and theirrole within embodiments of the present disclosure will be discussedbelow.

Turning to FIGS. 4A-4B, Process 400 of FIG. 4A and Process 450 of FIG.4B detail steps performed in accordance with some embodiments of thepresent disclosure for generating the most appropriate domain-specificresponse, as discussed herein. Process 400 of FIG. 4A details the stepsin generating domain-specific graphs corresponding to stylized lexiconsthat are utilized to generate the domain-specific response, and Process450 of FIG. 4B details the steps performed by a computer(s) generatingsuch response.

As discussed herein with reference to Processes 400 and 450, a regularchatbot response is determined, which is a response without any stylized(or stylistic) elements added. As discussed above, for purposes ofexplanation with regard to Process 400 and 450, the transformation ofthe regular response will be detailed with respect to twodomains—politics and entertainment. As discussed in detail below, thetransformation of the regular response to the domain-specific, stylizedresponse will retain the factual content of the regular response, butadd a distinctive style such that a user can easily identify andattribute the response to a specific domain.

By way of a non-limiting example, as illustrated in FIG. 5, a regularchatbot response for an inquiry 502 related to the weather, for example,“How is the weather”, would typically involve the following text, forexample: “It is very hot today”—item 504. The response 504 would betransformed into a domain-specific response based on the steps outlinedin the below disclosure related to Processes 400 and 450.

For example, for an entertainment-type domain (e.g., fashionista domain,for example), the regular response 504 of “It is very hot today” wouldbe transformed into a response 506: “Brace yourselves lovely people, itis kinda hot today! xoxo.” The factual nature that it is going to be hottoday remains in the transformed response 506; however, it has beenstylized from the perspective of how fashionistas would typically speak.For example, the words “Brace yourselves lovely people” “kinda” and“xoxo” have been added to the text of regular response 504, as has thepunctuation “,” and “!”; and the “.” at the end of the regular response504 has been changed. Also, the word “very” has been removed in view ofthe addition of “kinda”.

In another non-limiting example, for a political-type domain, theregular response 504 of “It is very hot today” would be transformed intoa response 508: “Ladies and gentlemen, it appears to be very hot today.Stay safe.” The factual nature that it is going to be hot today remainsin the transformed response 508; however, it has been stylized from theperspective of how politicians would typically speak. For example, thewords “Ladies and gentlemen”, “appears to be”, “very” and “stay safe”have been added to the text of regular response 504, as has thepunctuation “,“and”.”. Also, the word “is” from the regular response 504has been removed in view of the addition of “appears to be”.

Thus, as illustrated in FIG. 5, these example embodiments show how aregular response 504 is transformed according to two different domains:entertainment domain (as related to the transformed response 506) andpolitical domain (as related to the transformed response 508). Theresponses 506 and 508 have subtle differences based on stylisticelements of words used, formality in tone, and the like, derived fromthe learned styles of each domain, as discussed in detail below.

For purposes of this disclosure, in order to learn, understand orotherwise leverage the specific styles how vocabularies and wordpatterns of particular domains, the chatbot engine 300 relies onanalysis and identified and/or extracted data from Twitter® messages.While the discussion herein will focus on messages from Twitter®, itshould not be construed as limiting, as any type of network accessibleplatform/resource from which language processing can occur can beutilized as a basis for formulating the word-graphs, for example, butnot limited to, other social networking sites (e.g., Facebook®), email(e.g., Yahoo! Mail®), blogs, articles, instant messaging platforms(e.g., Yahoo! Messenger®, WhatsApp®, and the like), web portals, and thelike, or some combination thereof.

It is understood by those of ordinary skill in the art that Twitter®users constitute different types of personas, such as, for example,politicians, singers, actors, sports persons, and the like. Therefore,as discussed in more detail below, the chatbot engine utilizes tweets asa data source to model domain-specific styles. Identifying differencesin vocabularies and word-usage patterns across domains is critical inmodeling domain peculiarities and hence, differentiating betweendomains. For examples, tweets from fashionistas contain informallanguage (“xoxo,” and “ahhhhh,” for example) and heavy usage ofemoticons. In contrast, tweets from politicians are more formal. Byintroducing the peculiarities of a domain-specific style in a response,and keeping its existing factual content intact, the style of a specificdomain can be mimicked when outputting a chatbot response to aninquiring user. This intuition forms the core of disclosed methodology,as discussed herein.

Process 400 details the computerized techniques for constructingdomain-specific word-graphs using tweets posted from Twitter® accounts(and/or any other type of network accessible platform/resource thatenables learning/training of a system to understand language styles)that belong to users from specific domains, and using the graph togenerate word-patterns.

According to some embodiments, as discussed below, the chatbot engine300 constructs a domain-specific word-graph using tweets from Twitterhandles (accounts) belonging to a domain. In some embodiments, theword-graph for a domain is based upon a multi-sentence compressionapproach where the nodes represent words (along with part-of-speech(POS) tags) and the edges connect two adjacent words. In the instantdisclosure, this approach is extended and improved upon by constructinga reliable graph by ignoring edges and nodes which do not meet specificconstraints. In some embodiments, the infrequent edges in the set oftweets are removed, as discussed in more detail below. Traversing theword-graph from one node to another results in several paths which formcertain word-patterns. The chatbot engine 300 filters out patternscontaining nouns to prevent deviation from the actual information, andrestricts paths between pairs where the second word is an auxiliary verbto avoid introducing irrelevant patterns. Multiple scores are determinedand assigned to each pattern (e.g., importance score, contextualsimilarity score and linguistic quality score), as discussed below.

Then, the resultant word-graph for a domain is then utilized as part ofan Integer-Linear Programming (ILP) technique executed by the chatbotengine 300 in order to select the most appropriate patterns based on theabove mentioned scores and rewrite the regular response. This rewriting,or transformation of the regular response is disclosed in relation toProcess 450 which details the computerized steps for responding to aninquiry with a transformed response by leveraging the constructed wordgraphs from Process 400.

Turing first to FIG. 4A, Steps 402-420 of Process 400 are performed bythe word-graph construction module 304 of the chatbot engine 300.Process 400 begins with Step 402 where a domain representing a categoryof word usage patterns is identified. As discussed above, such wordusage patterns are related to ways or manners in which individuals speak(e.g., their personalities)—for example, politicians, sports reports,commentators, fashionistas, people in the entertainment industry and thelike. As a result of Step 402, messages, and for purposes of thisdisclosure (as discussed above), tweets from this domain are retrieved.For example, at least a sample of 100 k tweets from the domain (e.g.,100 k tweets from politicians during the 2016 Presidential Campaign).

In Step 404, each retrieved message is parsed in order to identify thecontent of each message. According to embodiments, for example, eachretrieved tweet is parsed, and the parsed data is analyzed in order toidentify the words (or identifiable character strings) within eachtweet.

In Step 406, each message and each message's content is analyzed inorder to determine a type of each message and the content containedtherein. The message type is determined because duplicate messages arediscarded from the retrieved set of messages. For example, “retweets”are discarded because they are duplicates of an original tweet. Thecontent type of each message is determined because messages can containany type of content, including, but not limited to, words, numbers,hashtags, or other symbols and identifiers, uniform resource locators(URLs), images, videos, and the like, and for purposes for tokenizingthe content, it may be required to identify the type of content, asdiscussed below.

In Step 408, based on the identified message types and content typescontained therein, the content of each message is tokenized and tagged.As understood by those of skill in the art, tokenization is a naturallanguage processing (NLP) process of breaking a stream of text up intoidentified words, phrases, symbols, or other meaningful elements andidentifying them as “tokens.” In some embodiments, such tokenization canbe performed by any known or to be known algorithm, technology ormechanism for tokenizing content, including, but not limited to, NLTKTreebank tokenizer technology. Based on the content type determination,all URLs, hashtags, numbers and other symbols or identifiers (e.g.,Twitter® handles) are modified into standard tokens. The words, ortokens as discussed herein, are then tagged with part-of-speech (POS)tags. For example, each token can be assigned a tag using aTwitter-specific POS tagger (or any other type of known or to be knownPOS tagger, or domain-specific tagger).

In Step 410, a word-graph for each message is constructed based on thecombination of each token and its POS tag. According to embodiments ofthe instant disclosure, the word-graph for each message comprises nodesand edges between the nodes. The node, which is a combination of a wordand its POS tag, represents a token and are iteratively added or mappedto the graph. An edge is created between two nodes if the correspondingtokens are adjacent in the original message (or tweet). In someembodiments, the adjacency direction between the nodes is maintained bythe graph having directed edges (showing the order of words in themessage). In some embodiments, adjacency can be bidirectional such thatthe edges are bidirectional between adjacent words.

In Step 412, the word-graphs for each message are mapped according totheir respective tokens and edges, and based on such mapping, abidirectional adjacency value is determined between tokens acrossmessages. According to embodiments of the instant disclosure, suchmapping is performed according to a predefined or predetermined set ofrules that dictate how tokens are added to a graph. For example, in someembodiments, if there are no nodes with the same corresponding word andPOS tag as w, a new node is created with token w. If there is only onenode in the graph with the same corresponding word and POS tag as w,then w is mapped to that node.

According to some embodiments, when there are multiple nodes with thesame word and POS tag as w, w is assigned to the node which has thehighest contextual similarity with w. Contextual similarity, asdiscussed in more detail below, is a value representing a number ofcommon words within a window of one word on either side of the nodes andthe current token (w) in a tweet. If multiple nodes have the samecontextual similarity with w, then w is assigned randomly to one ofthose nodes. If contextual similarity is zero for all the nodes, a newnode with w is created as a token. Such determinations of context ofwords and messages, and their contextual similarity enable the chatbotengine 300 to avoid spurious mappings of words to existing nodes.

In some embodiments, adjacency between two tokens across tweets can bebidirectional; therefore, the chatbot engine 300 can execute thefollowing strategy to maintain the acyclic nature of the graph. Forexample, assuming a tweet with the following consecutive pair of writtenwords (referred to as a bigram): w₁ _(_)w₂, where w₁ and w₂ are the twotokens in the bigram. For this adjacency, there will be a directed edgefrom node n₁ to node n₂, whose corresponding tokens are w₁ and w₂,respectively. In embodiments where there is a tweet having a reversebigram, i.e., w₂ _(_)w₁, then to avoid forming a cycle between nodes n₁and n₂, w₂ is mapped to n₂ using the above mentioned criteria, but w₁ isnot mapped to n₁ even if the mapping criteria are met. Either a new nodefor w₁ is created or w₁ is assigned to another node (other than n₁)depending upon whether the mapping criteria are met or not.

In Step 414, a word-graph for the domain, or specific to the domain, isconstructed based on the mapping of each message's word-graphs. By wayof a non-limiting example, using the following example tweets in thepolitics domain:

1. “We will win in 2016 because we are going to create an unprecedentedgrassroots movement.”

2. “The only way we can win is if enough people come together to joinour movement. So, are you in?”

As can be seen above, the tweets have common words such as “win” and“movement”. Merging the sentences along the words would result inseveral new possible patterns between pairs of words. For example: apattern—“unprecedented grassroots movement. So, are you in?” between“unprecedented” and “in”, that did not exist in any of the two tweetsbut is now generated as a result of fusion between both the tweets.According to some embodiments, as discussed herein, two dummy nodes(-start- and -end-) can be introduced to map the beginning and end ofall the tweets.

In Step 416, the constructed word-graph from Step 414 is pruned based onanalysis of the nodes and edges in the domain word-graph according todetermined (or predetermined) constraints. One of ordinary skill in theart would understand that constructing a domain word-graph usingadjacency relations between the words in all the messages from a domainresults in a large number of edges. Not all the edges are very frequent(satisfying an occurrence threshold), and may contain grammaticallyincorrect sequences due to the general informal style of tweets. Assuch, a significant number of such edges are determined to be irrelevantand should be removed. Therefore, in order to favor relevant andgrammatically correct word patterns, the chatbot engine 300 executes apruning function at both node and edge levels. According to someembodiments, the nodes that have less than a predetermined number ofedges (e.g., a constraint indicating a minimum number of edges: 5 edges,including both outgoing and incoming edges) are removed from the domainword-graph.

In Step 418, an edge weight is computed for each of the remaining edgesin the domain word-graph. In Step 420, a further pruning step isperformed based on the determined edge weights, such that the edges withedge weights lower than a threshold value (e.g., the t^(th) percentilevalue) are removed from the domain word-graph.

According to some embodiments, the edge weight W can be computed asfollows:

$\begin{matrix}{{{W( e_{ij} )} = \frac{{freq}( {w_{i}w_{j}} )}{{{freq}( w_{i} )}*{{freq}( w_{j} )}}},} & ( {{Eq}.\mspace{14mu} 1} )\end{matrix}$

where W(e_(ij)) denotes the weight of edge e_(ij) between nodes i and jwith corresponding tokens w₁ and w₂, respectively; and freq denotes thefrequency. From Eq. 1, the numerator computes the frequency ofco-occurrence of tokens w₁ and w₂, and the denominator computes theunigram frequencies of w₁ and w₂.

As such, as a result of Steps 402-420 of Process 400, a domain-specificword-graph is compiled that is ready to use on a regular response inorder to insert stylized words that enables the transformed response tomimic a personality associated with the domain, as discussed below inrelation to Process 450 of FIG. 4B.

Turning now to Process 450 of FIG. 4B, Steps 452-454 are performed bythe query module 302 of the chatbot engine 200; Steps 454-472 areperformed by the transformation module 306; and Step 474 is performed bythe output module 308. Process 450 details the steps for transforming aregular response from a chatbot by introducing and inserting relevantword patterns between existing words of the response without modifyingits factual content. Such transformation is modeled as an Integer-LinearProgramming (ILP) problem.

Process 450 begins with Step 452 where an input is received from a userin relation to a chatbot that includes a query. For example, a user canbe viewing a webpage and in response to a chatbot dialog box beingdisplayed on the page, the user can enter a query, which can include astring of characters. As understood by those of skill in the art, theinput can be any type of input, including, but not limited to, acharacter string of text, numbers or symbols, a URL(s), image content,video content, voice or audio content, longitude and latitudecoordinates, global positioning system (GPS) data, and the like, or somecombination thereof. Thus, Step 402 involves the entering of the queryand the input that triggers a search to be performed for a chatbotregular response to the query.

In Step 454, in response to the query, the chatbot engine 300 searchesan associated database (or another resource location on the internet)for a response to the query. Searching and identification of the properchatbot response can be performed by any known or to be known chatbot,or chatbot executed technology, such as, but not limited to, NLP, n-gramanalysis, vector translation and analysis, and the like, or somecombination thereof. Therefore, as a result to Step 454, the chatbotengine 300 determines, retrieves or otherwise identifies a regularresponse to the query, which includes a string or sequence of words.

In Step 456, the chatbot engine 300 tokenizes the regular response inorder to identify the individual words included in the regular response.According to some embodiments, such tokenization is performed in asimilar manner as discussed above in relation to Step 408 of Process400.

In Step 458, based on the identified words identified from thetokenization of the regular response occurring in Step 456, a search ofa repository is performed in order to determine, retrieve or otherwiseidentify a set of synonyms for each word in the regular response.According to some embodiments, the number of synonyms for each word canbe capped at a preset limit so as not to have an unequal number ofsynonyms for particular words.

In some embodiments, whether a synonym for a word is identified is basedon the type of word. That is, the chatbot engine 300 can implement basicsyntactic rules to improve grammatical correctness of the generated wordpatterns in the final output (of Process 450). For example, if a secondor subsequent word in a word pair (bigram) is an auxiliary verb (suchas, for example, “is” or “are”), then a synonym for such word may not beidentified, as no new word would be introduced between the pair. Withoutsuch constraint, there is a high probability that several irrelevantwords could be introduced between the stopwords that result in anincoherent output.

In Step 460, a set of bigrams is created based on the combinations ofwords in the regular response and in the determined set of synonyms. Insome embodiments, the created bigrams, embodied as an extended set ofbigrams from the bigrams of the regular response, are constructed suchthat the synonyms of a word are connected. In some embodiments, theextended bigrams are constructed such that all possible combinations ofwords are realizable from the constructed bigram.

In Step 462, a domain related to the regular response (or chatbot) isidentified. For example, if the query was entered in a domain related topolitical news, a political domain is identified. In another example,the context of the regular response (and/or the query) can be determined(e.g., by parsing the text of the response/query and determining itstopic) and then identifying a domain related to such topic. As a resultof Step 462, a domain-specific word-graph is identified that is relatedto the identified domain. Such domain-specific word-graph is the graphconstructed in Process 400, discussed above.

In Step 464, for each bigram constructed in Step 460, word patterns aredetermined, obtained or otherwise identified between the words of thebigram from the domain word-graph associated with the identified domain.According to some embodiments, the identification of the word patternscan be performed by traversing the graph from one node to another inorder to identify a path between the words in the bigram, andidentifying the words between the bigram words on such path. Forexample, if a bigram includes the words “hot, today”, then the firststep is to identify the word “hot” in the domain word-graph, thentraverse the graph until the word “today” is located; then, identify theword pattern (or sequence of words) that appear along that path. In someembodiments, such word pattern identification can be performed accordingto the mapping steps discussed above in relation to Step 412 discussedabove.

In some embodiments, the maximum number of words in the identified wordpattern can be restricted to a value K in order to prevent a majordeviation from the original meaning of the regular response. In suchembodiments, should a word pattern (or sequence or path) have more thanK words, then it can be discarded.

In Step 466, the chatbot engine 300 then computes word pattern scoresfor each obtained word pattern, and identifies a subset of word patternsbased on their scores.

By way of a non-limiting example, a regular response contains thefollowing ordered set of words w₁, w₂, w₃ . . . w_(m). Two consecutivewords in the above set of words are: w_(i) and w_(j), where j=i+1 (whichindicates that the max pattern to be inserted within each word is 1).Each pattern between the pair of words w_(i) and w_(j) is denoted byp^(q) _(ij). As discussed above, patterns are obtained using adomain-specific word graph structure constructed using tweets that areassociated with the domain. Each pattern has several scores associatedwith it including: importance, contextual similarity and linguisticquality:

Importance (I(p^(q) _(ij))): Informativeness or importance of thepattern computed using average co-occurrence scores between every pairof words from the domain-specific word-graph. The value is obtainedusing Eq. 1 discussed above.

Contextual Similarity (Sim(p^(q) _(ij))): Contextual similarity iscomputed as the cosine similarity between the paragraph vector of theregular response and the paragraph vector of the pattern. Generatedpatterns should be contextually relevant to the original regularresponse otherwise the final response may be incoherent and vague. Forexample, when transforming the response “bond market”, it should includepatterns that fit into the context of the “financial bond sector” andnot a “bond movie”. To obtain contextually relevant patterns, thechatbot engine 300 computes similarities between the original regularresponse and the generated patterns from the domain word-graph. In someembodiments, the regular response and the patterns can be represented asvector representations using, for example, Paragraph2Vec, where thecosine similarities between the regular response and each pattern arecomputed. The patterns with higher cosine similarities are ranked higherand the transformation of the regular response is only based on the topn patterns (e.g., top 5 patterns).

Linguistic Quality (LQ(p^(q) _(ij))): Indicator of grammaticality thatassigns a score of linguistic confidence to a sequence of words usingtwo language models trained, for example, on news and Twitter corpora.Such language modelling scores more probable sequences higher than thesequences that have lesser chances of occurring in the dataset.

According to some embodiments, the chatbot engine 300 may restrictdeviation in sentiment in the modified response by limiting only thosepatterns whose sentiment levels were low. In other words, markeddifferences in opinions (or facts) between the original response andrewritten response are to be avoided. In some embodiments, patterns thathad linguistic quality values between −2 to +2 (negative to positive)are used in order to ensure the same factual nature of the response ismaintained upon transformation.

In Step 468, a dependency parser confidence score is determined based atleast in part on the computed pattern scores (from Step 466), whichaccounts for overall grammaticality of the word patterns. In someembodiments, the dependency parser confidence score is computed usingthe Stanford Dependency Parser. The dependency parser confidence scoreis computed using the following equation:

$\begin{matrix}{{ {F = {{{D(k)}*y_{k}} + {\sum\limits_{i,{j \in k},{j = {i + 1}}}{{I( p_{ij}^{q} )}*{{Sim}( p_{ij}^{q} )}*{{LQ}( p_{ij}^{q} )}}}}} )*p_{ij}^{q}},} & ( {{Eq}.\mspace{14mu} 2} )\end{matrix}$

where D(k) is the dependency parser confidence score for sentence y_(k).The number of sentences depends on the number of words in the regularresponse and also the number of patterns between each pair of words.Hence, if there are a, b, c and d number of patterns between all theword pairs respectively, a maximum of a*b*c*d sentences can beconstructed. Due to this exponential nature, the number of patternsbetween pairs of words is restricted using an approach only keeping thetop N patterns as retained. In some embodiments, for example, only thetop two patterns are used.

In Step 470, the word pattern(s) with the highest dependency parserconfidence score is selected, and such selection is according to adetermined constraint that ensures that the selected pattern is the mostrelevant and topical sequence of words in relation to the regularresponse. In some embodiments, the following constraint is utilized:∀p _(ij) ^(q) between w _(i) and w _(j) ,Σp _(ij) ^(q)=1,   (Eq. 3).

For example, the sentences (y_(k)) are a result of combination of wordpatterns. Only one of the sentences can be selected out of all thepossible sentences. Therefore, only a single combination of patternsbetween the first and the last word is selected corresponding to theselected sentence and all other combinations are discarded. To modelthis constraint, each y_(k) is represented as a combination of patternsΠi,j∈s . . . e,q∈1 . . . max_(q)p_(ij) ^(q), selecting only one patternat a time between consecutive words. Here, s and e refer to two indicescorresponding to “dummy” start and end words respectively. In someembodiments, the introduction of dummy words allows for introducingpatterns at the beginning and end of the response. For example, if thereare two words in a sentence, w₁ and w₂, the addition of the start andend dummy words creates a sequence w_(s), w₁, w₂ and w_(e). The patternsbetween the first two words would be represented as p¹ _(s1), p² _(s1),etc. Therefore, for example, one of the possible combinations ofpatterns between words would be p¹ _(s1), p¹ ₁₂, p¹ ₂₃, p¹ _(3e), wherethe first patterns between adjacent words are combined, which representsy₁, the first modified sentence. Each sentence is represented as theproduct of the patterns that constructed it:

$\begin{matrix}{{\forall k},{{\forall{q \in {\lbrack {1\ldots\mspace{14mu}{num\_ patterns}} \rbrack y_{k}}}} = {\prod\limits_{i,{j \in k},{j = {i + 1}}}p_{ij}^{q}}},} & {( {{Eq}.\mspace{14mu} 4} ).}\end{matrix}$

The num_patterns in Eq. 4 refers to the total number of patterns used toconstruct a sentence, which in the example above is equal to 4.According to some embodiments, since each sentence is represented as aproduct of several patterns, the chatbot engine 300 is creatingnon-linear equation. However, since ILP constraints need to be linear,the non-linearity is converted to linearity using simpletransformations. As the variables are all binary, each y_(k) istransformed using the pattern variables and linear constraints.

For example, having y₁=p¹ _(s1), p¹ ₁₂, P¹ ₂₃, p¹ _(3e), the non-linearconstraint can be rewritten as follows:

$\begin{matrix}{{y_{1}<=p_{s\; 1}^{1}}{y_{1}<=p_{12}^{1}}{y_{1}<=p_{23}^{1}}{y_{1}<=p_{3e}^{1}}{{y_{1}>={p_{s\; 1}^{1} + p_{12}^{1} + p_{23}^{1} + p_{3e}^{1} - ( {{num\_ patterns} - 1} )}},}} & {( {{Eq}.\mspace{14mu} 5} ).}\end{matrix}$

Eq. 5 constrains the sentence variable such that it is equal to 1 if andonly if all the associated patterns are equal to 1; otherwise it isequal to 0. Thus, as in Step 470, in some embodiments, only m thesentences/patterns can be selected (per bigram), and therefore, it isadded as a constraint to the ILP, which is represented by the followingequation:

$\begin{matrix}{{{\sum\limits_{k}y_{k}} = 1},} & {( {{Eq}.\mspace{14mu} 6} ).}\end{matrix}$

Solving the ILP along with the above mentioned constraints generates theoptimal patterns used to modify the regular response. However, accordingto some embodiments, with the input sentence from the regular response,the complexity of the system continues to grow exponentially as theproducts of patterns can result in multiple sentences. Therefore, insome embodiments, a novel approach is applied by the chatbot engine 300where only a predetermined number of patterns are selected between eachset of words. This threshold value is a product of the three scoresassigned to the patterns to obtain a ranked list, and only the top mpatterns for the ILP formulation are chosen—where, for example, m is setto 2. In some embodiments, the maximum length of the pattern is set to athreshold value L (where L, for example, can be set to 2).

Therefore, in Step 472, the patterns selected by ILP are used to fillthe gaps between adjacent words (or bigrams) and a revised response isgenerated. That is, a top ranked pattern is selected, as discussedabove, and in Step 472, the selected pattern is inserted into theregular response, thereby modifying the response. Thismodified/transformed regular response is then output to the user inresponse to the query received in Step 452. Step 474. This response isoutput to the user for display within a user interface (UI) associatedwith the chatbot.

By way of another non-limiting example, based on the above discussionsof Process 450 leveraging the domain word-graph constructed by Process400, a regular response of “He is a loser” will be transformed using thedomain word-graph built using tweets (e.g., from the Twitter® Firehose)from the entertainment domain (for example, from tweets sent by usersfrom the entertainment industry). The following are the pairs of wordsbetween which patterns would be introduced: (i) -start-, he (ii) is, a(iii) a, loser (iv) loser, -end-. As discussed above, the -start- and-end- tokens are dummy tokens used to mark the start and end of theinput. As a result, patterns can, in some embodiments, also beintroduced before the first word and after the last word in theresponse. Given a particular domain word graph, example patterns betweeneach pair of words are as follows: “is literally”, “a total loser, loser!!! xoxoxo”. Combining all the suggested patterns, results in thefollowing sequence: “He is literally a total loser !!! xoxoxo.” Here,the input sentence is significantly transformed to reflect the casualwriting style used in tweets from users that belong to the entertainmentdomain.

FIG. 6 is a work flow example 600 for serving relevant digital mediacontent associated with or comprising advertisements (e.g., digitaladvertisement content) based on the information associated with agenerated chatbot response, as discussed above in relation to FIGS. 3-5.Such information, referred to as “chatbot response information” forreference purposes only, can include, but is not limited to, informationassociated with the query or question asked by a user, informationrelated to the requesting user, the domain associated with the response,the new words added to the response, the style added to the originalresponse, and the like, and/or some combination thereof.

As discussed above, reference to an “advertisement” should be understoodto include, but not be limited to, digital media content that providesinformation provided by another user, service, third party, entity, andthe like. Such digital ad content can include any type of known or to beknown media renderable by a computing device, including, but not limitedto, video, text, audio, images, and/or any other type of known or to beknown multi-media. In some embodiments, the digital ad content can beformatted as hyperlinked multi-media content that provides deep-linkingfeatures and/or capabilities. Therefore, while the content is referredas an advertisement, it is still a digital content item that isrenderable by a computing device, and such digital content itemcomprises digital content relaying proprietary or promotional contentprovided by a network associated third party.

In Step 602, chatbot response information is identified. As discussedabove, the chatbot response information can be based any of theinformation from processes outlined above with respect to FIGS. 3-5. Forpurposes of this disclosure, Process 600 will refer to single chatbotresponse as the basis for serving a digital advertisement(s); however,it should not be construed as limiting, as any number of responses,queries and the like, as well as programs used during the chatbotresponse generation/transformation can form such basis, withoutdeparting from the scope of the instant disclosure.

In Step 604, a context is determined based on the identified chatbotresponse information. This context forms a basis for servingadvertisements related to the chatbot response information. In someembodiments, the context can be based on a determined category which thechatbot response information of Step 602 represents. For example, thechatbot response can include content associated with a categorycorresponding to “fashion”; therefore, the context identified in Step604 can be related to “fashion” or other “clothing trends” and can beleveraged in order to identify digital ad content of interest (forexample, a digital ad providing a promotion for a discount at a local(to the user's geographic location) department store), as discussedherein in relation to the steps of Process 600. In some embodiments, theidentification of the context from Step 604 can occur before, duringand/or after the analysis detailed above with respect to Process 400(and its sub-parts), or some combination thereof.

In Step 606, the determined context is communicated (or shared) with anadvertisement platform comprising an advertisement server 130 and addatabase. Upon receipt of the context, the advertisement server 130performs (e.g., is caused to perform as per instructions received fromthe device executing the chatbot engine 300) a search for a relevantadvertisement within the associated ad database. The search for anadvertisement is based at least on the identified context.

In Step 608, the advertisement server 130 searches the ad database for adigital advertisement(s) that matches the identified context. In Step610, an advertisement is selected (or retrieved) based on the results ofStep 608. In some embodiments, the selected advertisement can bemodified to conform to attributes or capabilities of the page,interface, message, platform, application or method upon which theadvertisement will be displayed, and/or to the application and/or devicefor which it will be displayed. In some embodiments, the selectedadvertisement is shared or communicated via the application the user isutilizing to search, view and/or render the chatbot response. Step 612.In some embodiments, the selected advertisement is displayed within aportion of the interface or within an overlaying or pop-up interfaceassociated with the interface used to enter the query and receive/outputthe chatbot response.

As shown in FIG. 7, internal architecture 700 of a computing device(s),computing system, computing platform and the like includes one or moreprocessing units, processors, or processing cores, (also referred toherein as CPUs) 712, which interface with at least one computer bus 702.Also interfacing with computer bus 702 are computer-readable medium, ormedia, 706, network interface 714, memory 704, e.g., random accessmemory (RAM), run-time transient memory, read only memory (ROM), mediadisk interface 708 and/or media disk drive interface 720 as an interfacefor a drive that can read and/or write to media including removablemedia such as floppy, CD-ROM, DVD, media, display interface 710 asinterface for a monitor or other display device, keyboard interface 716as interface for a keyboard, pointing device interface 718 as aninterface for a mouse or other pointing device, and miscellaneous otherinterfaces 722 not shown individually, such as parallel and serial portinterfaces and a universal serial bus (USB) interface.

Memory 704 interfaces with computer bus 702 so as to provide informationstored in memory 704 to CPU 712 during execution of software programssuch as an operating system, application programs, device drivers, andsoftware modules that comprise program code, and/or computer executableprocess steps, incorporating functionality described herein, e.g., oneor more of process flows described herein. CPU 712 first loads computerexecutable process steps from storage, e.g., memory 704, computerreadable storage medium/media 706, removable media drive, and/or otherstorage device. CPU 712 can then execute the stored process steps inorder to execute the loaded computer-executable process steps. Storeddata, e.g., data stored by a storage device, can be accessed by CPU 712during the execution of computer-executable process steps.

Persistent storage, e.g., medium/media 706, can be used to store anoperating system and one or more application programs. Persistentstorage can also be used to store device drivers, such as one or more ofa digital camera driver, monitor driver, printer driver, scanner driver,or other device drivers, web pages, content files, playlists and otherfiles. Persistent storage can further include program modules and datafiles used to implement one or more embodiments of the presentdisclosure, e.g., listing selection module(s), targeting informationcollection module(s), and listing notification module(s), thefunctionality and use of which in the implementation of the presentdisclosure are discussed in detail herein.

Network link 728 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 728 mayprovide a connection through local network 724 to a host computer 726 orto equipment operated by a Network or Internet Service Provider (ISP)730. ISP equipment in turn provides data communication services throughthe public, worldwide packet-switching communication network of networksnow commonly referred to as the Internet 732.

A computer called a server host 734 connected to the Internet 732 hostsa process that provides a service in response to information receivedover the Internet 732. For example, server host 734 hosts a process thatprovides information representing image and/or video data forpresentation at display 710. It is contemplated that the components ofsystem 700 can be deployed in various configurations within othercomputer systems, e.g., host and server.

At least some embodiments of the present disclosure are related to theuse of computer system 700 for implementing some or all of thetechniques described herein. According to one embodiment, thosetechniques are performed by computer system 700 in response toprocessing unit 712 executing one or more sequences of one or moreprocessor instructions contained in memory 704. Such instructions, alsocalled computer instructions, software and program code, may be readinto memory 704 from another computer-readable medium 706 such asstorage device or network link. Execution of the sequences ofinstructions contained in memory 704 causes processing unit 712 toperform one or more of the method steps described herein. In alternativeembodiments, hardware, such as ASIC, may be used in place of or incombination with software. Thus, embodiments of the present disclosureare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link and other networks throughcommunications interface, carry information to and from computer system700. Computer system 700 can send and receive information, includingprogram code, through the networks, among others, through network linkand communications interface. In an example using the Internet, a serverhost transmits program code for a particular application, requested by amessage sent from computer, through Internet, ISP equipment, localnetwork and communications interface. The received code may be executedby processor 702 as it is received, or may be stored in memory 704 or instorage device or other non-volatile storage for later execution, orboth.

For the purposes of this disclosure a module is a software, hardware, orfirmware (or combinations thereof) system, process or functionality, orcomponent thereof, that performs or facilitates the processes, features,and/or functions described herein (with or without human interaction oraugmentation). A module can include sub-modules. Software components ofa module may be stored on a computer readable medium for execution by aprocessor. Modules may be integral to one or more servers, or be loadedand executed by one or more servers. One or more modules may be groupedinto an engine or an application.

For the purposes of this disclosure the term “user”, “subscriber”“consumer” or “customer” should be understood to refer to a user of anapplication or applications as described herein and/or a consumer ofdata supplied by a data provider. By way of example, and not limitation,the term “user” or “subscriber” can refer to a person who receives dataprovided by the data or service provider over the Internet in a browsersession, or can refer to an automated software application whichreceives the data and stores or processes the data.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible.

Functionality may also be, in whole or in part, distributed amongmultiple components, in manners now known or to become known. Thus,myriad software/hardware/firmware combinations are possible in achievingthe functions, features, interfaces and preferences described herein.Moreover, the scope of the present disclosure covers conventionallyknown manners for carrying out the described features and functions andinterfaces, as well as those variations and modifications that may bemade to the hardware or software or firmware components described hereinas would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure.

What is claimed is:
 1. A method comprising: receiving, at a computingdevice, a request from a user, said request input by the user respectiveto a chatbot provided by the computing device, said request comprising asequence of words; searching, via the computing device, a database basedon said request, said search comprising identifying, within saiddatabase, a first response comprising a sequence of words; tokenizing,via the computing device, said first response, said tokenizingcomprising identifying each identified word from said tokenizing of thefirst response as a token and tagging each token; creating, via thecomputing device, a set of bigrams based at least in part upon thetokenized first response, said bigrams comprising adjacent words in saidfirst response; determining, via the computing device, a set of wordpatterns between each bigram based on a domain word-graph, said domainword-graph comprising nodes and edges, wherein each node corresponds toa word and each edge is a directional link between the nodes, saiddetermination comprising determining a path within said domainword-graph between the words in each bigram, and identifying, based onsaid determined path, a set of word patterns along the path for eachbigram; computing, via the computing device, scores for each wordpattern in said set of word patterns, said computed scores indicating aco-occurrences, contextual similarity and linguistic quality between thewords in the bigrams and the word patterns; selecting, via the computingdevice, a word pattern based on the computed scores; modifying, via thecomputing device, the first response by inserting the word pattern intosaid first response based on the bigram from which it was identified,said modification causing the creation of a second response; andcommunicating, via the computing device, said second response to saiduser in response to said request, for display of said communicatedsecond response within a user interface (UI) associated with thechatbot.
 2. The method of claim 1, further comprising: searching arepository of synonymous words based on said first response;identifying, based on said search, a set of synonyms for each word inthe first response.
 3. The method of claim 2, wherein said created setof bigrams includes the identified set of synonyms, wherein said set ofword patterns determination is based on said bigram set that includesthe identified set of synonyms.
 4. The method of claim 1, wherein saidcomputation of said co-occurrence score comprises: computing aco-occurrence score for each pair of words in said word patterns; andcomputing an average score based on each co-occurrence score.
 5. Themethod of claim 4, wherein said computation of said co-occurrence scorecorresponds to a computation of weights of each edge between each pair,wherein said weight computation comprises:${{W( e_{ij} )} = \frac{{freq}( {w_{i}w_{j}} )}{{{freq}( w_{i} )}*{{freq}( w_{j} )}}},$wherein W(e_(ij)) denotes a weight of edge e, between nodes i and j withcorresponding tokens w₁ and w₂, respectively; wherein freq denotes thefrequency; wherein the numerator computes a frequency of co-occurrenceof tokens w₁ and w₂; and wherein the denominator computes unigramfrequencies of w₁ and w₂.
 6. The method of claim 1, wherein saidcomputation of said contextual similarity comprises: computing a cosinesimilarity between a paragraph vector of the first response and aparagraph vector of each word pattern; ranking cosine similarities ofeach word pattern, wherein said selected word pattern is within apredetermined number of top ranked patterns.
 7. The method of claim 1,wherein said computation of said linguistic quality comprises: computinga score of linguistic confidence based on at least one language trainedmodel; and determining, based on the linguistic confidence scores, aprobability for each word pattern.
 8. The method of claim 1, furthercomprising: determining a dependency parser confidence score whichprovides an indication of overall grammaticality of each word pattern,said computation of the dependency parser confidence score comprising:${ {F = {{{D(k)}*y_{k}} + {\sum\limits_{i,{j \in k},{j = {i + 1}}}{{I( p_{ij}^{q} )}*{{Sim}( p_{ij}^{q} )}*{{LQ}( p_{ij}^{q} )}}}}} )*p_{ij}^{q}},$wherein D(k) is the dependency parser confidence score for each wordpattern; and wherein a number of word patterns for said D(k) computationis dependent on a number of words in the first response and a number ofpatterns between each pair of words.
 9. The method of claim 1, whereinsaid tagging is performed by the computing device executing apart-of-speech (POS) tagger associated with said domain.
 10. The methodof claim 1, further comprising: identifying a domain corresponding tosaid request, wherein said domain word-graph is associated with saididentified domain, said domain related to a type of personality ofspeech.
 11. The method of claim 1, further comprising: determining acontext based on said second response; causing communication, over thenetwork, of said context to a third party content platform to obtain adigital content item comprising third party digital content associatedwith said context; receiving, over the network, said digital contentitem; and causing display said digital content item in association withthe communication of said second response.
 12. A non-transitorycomputer-readable storage medium tangibly encoded withcomputer-executable instructions, that when executed by a processorassociated with a computing device, performs a method comprising:receiving, at the computing device, a request from a user, said requestinput by the user respective to a chatbot provided by the computingdevice, said request comprising a sequence of words; searching, via thecomputing device, a database based on said request, said searchcomprising identifying, within said database, a first responsecomprising a sequence of words; tokenizing, via the computing device,said first response, said tokenizing comprising identifying eachidentified word from said tokenizing of the first response as a tokenand tagging each token; creating, via the computing device, a set ofbigrams based at least in part upon the tokenized first response, saidbigrams comprising adjacent words in said first response; determining,via the computing device, a set of word patterns between each bigrambased on a domain word-graph, said domain word-graph comprising nodesand edges, wherein each node corresponds to a word and each edge is adirectional link between the nodes, said determination comprisingdetermining a path within said domain word-graph between the words ineach bigram, and identifying, based on said determined path, a set ofword patterns along the path for each bigram; computing, via thecomputing device, scores for each word pattern in said set of wordpatterns, said computed scores indicating a co-occurrences, contextualsimilarity and linguistic quality between the words in the bigrams andthe word patterns; selecting, via the computing device, a word patternbased on the computed scores; modifying, via the computing device, thefirst response by inserting the word pattern into said first responsebased on the bigram from which it was identified, said modificationcausing the creation of a second response; and communicating, via thecomputing device, said second response to said user in response to saidrequest, for display of said communicated second response within a userinterface (UI) associated with the chatbot.
 13. The non-transitorycomputer-readable storage medium of claim 12, further comprising:searching a repository of synonymous words based on said first response;identifying, based on said search, a set of synonyms for each word inthe first response, wherein said created set of bigrams includes theidentified set of synonyms, wherein said set of word patternsdetermination is based on said bigram set that includes the identifiedset of synonyms.
 14. The non-transitory computer-readable storage mediumof claim 12, wherein said computation of said co-occurrence scorecomprises: computing a co-occurrence score for each pair of words insaid word patterns; and computing an average score based on eachco-occurrence score.
 15. The non-transitory computer-readable storagemedium of claim 14, wherein said computation of said co-occurrence scorecorresponds to a computation of weights of each edge between each pair,wherein said weight computation comprises:${{W( e_{ij} )} = \frac{{freq}( {w_{i}w_{j}} )}{{{freq}( w_{i} )}*{{freq}( w_{j} )}}},$wherein W(e_(ij)) denotes a weight of edge e_(ij) between nodes i and jwith corresponding tokens w₁ and w₂, respectively; wherein freq denotesthe frequency; wherein the numerator computes a frequency ofco-occurrence of tokens w₁ and w₂; and wherein the denominator computesunigram frequencies of w₁ and w₂.
 16. The non-transitorycomputer-readable storage medium of claim 12, wherein said computationof said contextual similarity comprises: computing a cosine similaritybetween a paragraph vector of the first response and a paragraph vectorof each word pattern; ranking cosine similarities of each word pattern,wherein said selected word pattern is within a predetermined number oftop ranked patterns.
 17. The non-transitory computer-readable storagemedium of claim 12, wherein said computation of said linguistic qualitycomprises: computing a score of linguistic confidence based on at leastone language trained model; and determining, based on the linguisticconfidence scores, a probability for each word pattern.
 18. Thenon-transitory computer-readable storage medium of claim 12, furthercomprising: determining a dependency parser confidence score whichprovides an indication of overall grammaticality of each word pattern,said computation of the dependency parser confidence score comprising:${ {F = {{{D(k)}*y_{k}} + {\sum\limits_{i,{j \in k},{j = {i + 1}}}{{I( p_{ij}^{q} )}*{{Sim}( p_{ij}^{q} )}*{{LQ}( p_{ij}^{q} )}}}}} )*p_{ij}^{q}},$wherein D(k) is the dependency parser confidence score for each wordpattern; and wherein a number of word patterns for said D(k) computationis dependent on a number of words in the first response and a number ofpatterns between each pair of words.
 19. A computing device comprising:a processor; a non-transitory computer-readable storage medium fortangibly storing thereon program logic for execution by the processor,the program logic comprising: logic executed by the processor forreceiving, at the computing device, a request from a user, said requestinput by the user respective to a chatbot provided by the computingdevice, said request comprising a sequence of words; logic executed bythe processor for searching, via the computing device, a database basedon said request, said search comprising identifying, within saiddatabase, a first response comprising a sequence of words; logicexecuted by the processor for tokenizing, via the computing device, saidfirst response, said tokenizing comprising identifying each identifiedword from said tokenizing of the first response as a token and taggingeach token; logic executed by the processor for creating, via thecomputing device, a set of bigrams based at least in part upon thetokenized first response, said bigrams comprising adjacent words in saidfirst response; logic executed by the processor for determining, via thecomputing device, a set of word patterns between each bigram based on adomain word-graph, said domain word-graph comprising nodes and edges,wherein each node corresponds to a word and each edge is a directionallink between the nodes, said determination comprising determining a pathwithin said domain word-graph between the words in each bigram, andidentifying, based on said determined path, a set of word patterns alongthe path for each bigram; logic executed by the processor for computing,via the computing device, scores for each word pattern in said set ofword patterns, said computed scores indicating a co-occurrences,contextual similarity and linguistic quality between the words in thebigrams and the word patterns; logic executed by the processor forselecting, via the computing device, a word pattern based on thecomputed scores; logic executed by the processor for modifying, via thecomputing device, the first response by inserting the word pattern intosaid first response based on the bigram from which it was identified,said modification causing the creation of a second response; and logicexecuted by the processor for communicating, via the computing device,said second response to said user in response to said request, fordisplay of said communicated second response within a user interface(UI) associated with the chatbot.